Towards artifact-free non-invasive renal and hepatic functional MRI: development of an organ-specific navigator-based pCASL sequence with EPI acquisition

• Applicant: Dr. Ke Zhang
• Subject Area: Radiology ; Medical Physics, Biomedical Technology
• Term: from 2022 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 507778602

Subtle local changes in renal and hepatic perfusion are important indicators of developing disease; if quantified, they can be used to grade malignancy in neoplastic processes, and to monitor organ function during disease treatment. While glomerular filtration rate is considered the gold standard of kidney function evaluation, it has no spatial correlate, which is why cross-sectional perfusion measurements are more accurate and specific. Considering that the administration of nephrotoxic contrast agent is ill-advised in patients with severe kidney function impairment and patients with allergies to contrast agents, non-invasive MRI perfusion measurements with arterial spin labelling (ASL) techniques are gaining in importance. They provide quantitative measurements of organ perfusion by using magnetically labeled blood as an endogenous tracer. In neuroimaging, pseudocontinuous ASL (pCASL) has been developed recently and shown to provide a relatively reliable assessment of cerebral perfusion. In the abdominal imaging setting, however, motion artifacts significantly complicate the application of the pCASL technique. To address this problem, we aim to develop an organ-specific navigator-based slice tracking technique using an Echo-Planar Imaging (EPI) based sequence in renal and hepatic perfusion measurements to prospectively compensate for respiratory motion artifacts. This will allow an artifact-free free-breathing multi-slice and non-invasive renal and hepatic perfusion imaging. The sequence will be developed for a 1.5 Tesla MRI scanner (Siemens Aera), validated in volunteers, and compared against standard ASL MRI techniques. Postprocessing, numerical analysis, and statistical evaluation will be performed in Matlab v9.11 R2021b, Python v3.10.2, and R v4.1.0.

• DFG Programme: Research Grants
• Co-Investigator: Professor Felix Kurz, Ph.D.